Leadership

In my previous article, “25 Definitions of Fairness in AI,” I dove into the various forms of bias that can manifest in AI systems and how different definitions of fairness aim to address these biases. If you haven’t had a chance to read it, I highly recommend doing so, as it sets the stage for the deeper exploration we’re about to embark on. It wasn’t long ago that I was sitting in a meeting with a group of engineers discussing

Understanding Bias and Fairness in AI: More Complex Than We Think We’ve all heard about bias in AI and the need for fairness in these systems, but the topic is much broader than it seems. When we talk about AI bias, it’s not just about addressing a single issue like racial or gender bias. Fairness in AI is multi-dimensional, and the decisions we make when designing these systems have real-world consequences for every aspect of society. I realized this firsthand